Efficient online model-based design of experiments via parameter subset selection for batch dynamical systems

Model-based design of experiments (MBDOE) is being widely used for the efficient identification of complex dynamical systems. Given real-time measurements, online MBDOE can be formulated. However, conventional real-time MBDOE requires considerable computational time for finding a solution which makes real-time implementation impossible. Moreover, the optimality of experimental design and the accuracy of parameter estimates are not ensured. We propose a new algorithm that advances online MBDOE by focusing on the subset of parameters at each design instant. It considerably reduces the numerical complexity of the problem while almost completely preserving its optimality and allowing for faster and more accurate calculation. A case study is presented, wherein the proposed algorithm is applied to a fed-batch bioreactor model with 14 parameters. (C) 2018 Elsevier Ltd. All rights reserved.